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Causal deep learning for enhancing explainability in 6G network edge intelligence anomaly detection.

Xiao Yi1, Zengri Zeng1,2, Ming Dai3

  • 1Hunan University of Humanities Science and Technology, LouDi, 417000, China.

Scientific Reports
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a new framework for 6G edge intelligence anomaly detection, combining causal inference and LSTM networks. It enhances system interpretability and trustworthiness for reliable cybersecurity decisions.

Area of Science:

  • Network security
  • Artificial intelligence
  • Causal inference

Background:

  • 6G network development presents challenges for edge intelligence, particularly in system interpretability and trustworthiness.
  • Machine learning methods for anomaly detection often act as black boxes, hindering reliable cybersecurity decision support.

Purpose of the Study:

  • To develop a novel framework for anomaly detection in 6G edge intelligence that integrates causal inference with LSTM networks.
  • To improve the interpretability and trustworthiness of anomaly detection systems for enhanced cybersecurity.

Main Methods:

  • Random Fourier Feature transformation to eliminate nonlinear feature correlations, a prerequisite for causal analysis.
  • Sample-weighted adjustments to quantify feature-specific causal effects and ensure model stability.
Keywords:
6G NEICausal deep learningCybersecurityGANsRFF

Related Experiment Videos

  • Generative Adversarial Networks (GANs) for generating high-quality minority-class samples to augment training data.

    • Main Results:

    • Demonstrated a 33.7% improvement in explainability for anomaly detection.
    • Achieved a 68% reduction in root-cause localization time.
    • Enhanced overall anomaly detection accuracy through data augmentation.

    Conclusions:

    • The proposed framework establishes a new paradigm for cybersecurity in 6G edge intelligence by leveraging causal reasoning.
    • Integrating causal inference with LSTM networks significantly improves interpretability and reduces localization time in anomaly detection.